Exercise Mitigates the Loss of Muscle Mass by Attenuating the Activation of Autophagy during Severe Energy Deficit
Bibliographic record
Abstract
The loss of skeletal muscle mass with energy deficit is thought to be due to protein breakdown by the autophagy-lysosome and the ubiquitin-proteasome systems. We studied the main signaling pathways through which exercise can attenuate the loss of muscle mass during severe energy deficit (5500 kcal/day). Overweight men followed four days of caloric restriction (3.2 kcal/kg body weight day) and prolonged exercise (45 min of one-arm cranking and 8 h walking/day), and three days of control diet and restricted exercise, with an intra-subject design including biopsies from muscles submitted to distinct exercise volumes. Gene expression and signaling data indicate that the main catabolic pathway activated during severe energy deficit in skeletal muscle is the autophagy-lysosome pathway, without apparent activation of the ubiquitin-proteasome pathway. Markers of autophagy induction and flux were reduced by exercise primarily in the muscle submitted to an exceptional exercise volume. Changes in signaling are associated with those in circulating cortisol, testosterone, cortisol/testosterone ratio, insulin, BCAA, and leucine. We conclude that exercise mitigates the loss of muscle mass by attenuating autophagy activation, blunting the phosphorylation of AMPK/ULK1/Beclin1, and leading to p62/SQSTM1 accumulation. This includes the possibility of inhibiting autophagy as a mechanism to counteract muscle loss in humans under severe energy deficit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".